---
title: ChatPerplexity
---

This guide will help you getting started with Perplexity [chat models](/oss/langchain/models). For detailed documentation of all `ChatPerplexity` features and configurations head to the [API reference](https://api.js.langchain.com/classes/_langchain_community.chat_models_perplexity.ChatPerplexity.html).

## Overview

### Integration details

| Class | Package | Local | Serializable | [PY support](https://python.langchain.com/docs/integrations/chat/perplexity/) | Downloads | Version |
| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |
| [`ChatPerplexity`](https://api.js.langchain.com/classes/_langchain_community.chat_models_perplexity.ChatPerplexity.html) | [`@langchain/community`](https://npmjs.com/@langchain/community) | ❌ | beta | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/community?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/community?style=flat-square&label=%20&) |

### Model features

See the links in the table headers below for guides on how to use specific features.

| [Tool calling](/oss/langchain/tools) | [Structured output](/oss/langchain/structured-output) | JSON mode | [Image input](/oss/how-to/multimodal_inputs/) | Audio input | Video input | [Token-level streaming](/oss/langchain/streaming/) | [Token usage](/oss/how-to/chat_token_usage_tracking/) | [Logprobs](/oss/how-to/logprobs/) |
| :---: | :---: | :---: | :---: |  :---: | :---: | :---: | :---: | :---: |
| ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ |

Note that at the time of writing, Perplexity only supports structured outputs on certain usage tiers.

## Setup

To access Perplexity models you'll need to create a Perplexity account, get an API key, and install the `@langchain/community` integration package.

### Credentials

Head to [https://perplexity.ai](https://perplexity.ai) to sign up for Perplexity and generate an API key. Once you've done this set the `PERPLEXITY_API_KEY` environment variable:

```bash
export PERPLEXITY_API_KEY="your-api-key"
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```bash
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### Installation

The LangChain Perplexity integration lives in the `@langchain/community` package:

```{=mdx}
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/community @langchain/core
</Npm2Yarn>

```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```typescript
import { ChatPerplexity } from "@langchain/community/chat_models/perplexity"

const llm = new ChatPerplexity({
  model: "sonar",
  temperature: 0,
  maxTokens: undefined,
  timeout: undefined,
  maxRetries: 2,
  // other params...
})
```

## Invocation

```typescript
const aiMsg = await llm.invoke([
  {
    role: "system",
    content: "You are a helpful assistant that translates English to French. Translate the user sentence.",
  },
  {
    role: "user",
    content: "I love programming.",
  },
])
aiMsg
```

```output
AIMessage {
  "id": "run-71853938-aa30-4861-9019-f12323c09f9a",
  "content": "J'adore la programmation.",
  "additional_kwargs": {
    "citations": [
      "https://careersatagoda.com/blog/why-we-love-programming/",
      "https://henrikwarne.com/2012/06/02/why-i-love-coding/",
      "https://forum.freecodecamp.org/t/i-love-programming-but/497502",
      "https://ilovecoding.org",
      "https://thecodinglove.com"
    ]
  },
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 20,
      "completionTokens": 9,
      "totalTokens": 29
    }
  },
  "tool_calls": [],
  "invalid_tool_calls": []
}
```

```typescript
console.log(aiMsg.content)
```

```output
J'adore la programmation.
```

## Chaining

We can [chain](/oss/how-to/sequence/) our model with a prompt template like so:

```typescript
import { ChatPromptTemplate } from "@langchain/core/prompts"

const prompt = ChatPromptTemplate.fromMessages(
  [
    [
      "system",
      "You are a helpful assistant that translates {input_language} to {output_language}.",
    ],
    ["human", "{input}"],
  ]
)

const chain = prompt.pipe(llm);
await chain.invoke(
  {
    input_language: "English",
    output_language: "German",
    input: "I love programming.",
  }
)
```

```output
AIMessage {
  "id": "run-a44dc452-4a71-423d-a4ee-50a2d7c90abd",
  "content": "**English to German Translation:**\n\n\"I love programming\" translates to **\"Ich liebe das Programmieren.\"**\n\nIf you'd like to express your passion for programming in more detail, here are some additional translations:\n\n- **\"Programming is incredibly rewarding and fulfilling.\"** translates to **\"Das Programmieren ist unglaublich lohnend und erfüllend.\"**\n- **\"I enjoy solving problems through coding.\"** translates to **\"Ich genieße es, Probleme durch Codieren zu lösen.\"**\n- **\"I find the process of creating something from nothing very satisfying.\"** translates to **\"Ich finde den Prozess, etwas aus dem Nichts zu schaffen, sehr befriedigend.\"**",
  "additional_kwargs": {
    "citations": [
      "https://careersatagoda.com/blog/why-we-love-programming/",
      "https://henrikwarne.com/2012/06/02/why-i-love-coding/",
      "https://dev.to/dvddpl/coding-is-boring-why-do-you-love-coding-cl0",
      "https://forum.freecodecamp.org/t/i-love-programming-but/497502",
      "https://ilovecoding.org"
    ]
  },
  "response_metadata": {
    "tokenUsage": {
      "promptTokens": 15,
      "completionTokens": 149,
      "totalTokens": 164
    }
  },
  "tool_calls": [],
  "invalid_tool_calls": []
}
```

## API reference

For detailed documentation of all ChatPerplexity features and configurations head to the [API reference](https://api.js.langchain.com/classes/_langchain_community.chat_models_perplexity.ChatPerplexity.html).
